Bộ sách luyện thi ETS 2023 LC&RC
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Thể loại:Computers - Computer Science
Năm:2019
In lần thứ:Paperback
Nhà xuát bản:Addison-Wesley Professional
Ngôn ngữ:english
Trang:416 / 872
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"The authors' clear
visual style provides a comprehensive look at what's currently possible
with artificial neural networks as well as a glimpse of the magic that's
to come."--Tim Urban, author ofWait But WhyFully Practical, Insightful Guide to Modern Deep Learning
Deep
learning is transforming software, facilitating powerful new artificial
intelligence capabilities, and driving unprecedented algorithm
performance.Deep Learning Illustratedis uniquely intuitive
and offers a complete introduction to the discipline's techniques.
Packed with full-color figures and easy-to-follow code, it sweeps away
the complexity of building deep learning models, making the subject
approachable and fun to learn.
World-class instructor and
practitioner Jon Krohn--with visionary content from Grant Beyleveld and
beautiful illustrations by Agla� Bassens--presents straightforward
analogies to explain what deep learning is, why it has become so
popular, and how it relates to other machine learning approaches. Krohn
has created a practical reference and tutorial for developers, data
scientists, researchers, analysts, and students who want to start
applying it. He illuminates theory with hands-on Python code in
accompanying Jupyter notebooks. To help you progress quickly, he focuses
on the versatile deep learning library Keras to nimbly construct
efficient TensorFlow models; PyTorch, the leading alternative library,
is also covered.
You'll gain a pragmatic understanding of all major
deep learning approaches and their uses in applications ranging from
machine vision and natural language processing to image generation and
game-playing algorithms.
Discover what makes deep learning systems
unique, and the implications for practitioners Explore new tools that
make deep learning models easier to build, use, and improve Master
essential theory: artificial neurons, training, optimization,
convolutional nets, recurrent nets, generative adversarial networks
(GANs), deep reinforcement learning, and more Walk through building
interactive deep learning applications, and move forward with your own
artificial intelligence projectsRegister your book for convenient
access to downloads, updates, and/or corrections as they become
available. See inside book for details.
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